Users of chemical arrays such as nucleic acid microarrays, CGH arrays, arrays measuring protein abundance and the like need software packages to perform feature extraction, that is, to extract signal and/or log ratio data from the features on the arrays. Chemical array data may have flaws due to problems in “upstream” processes such as: array synthesis; target preparation (“prep”)/labeling; hybridization (“hyb”)/wash; scanning; and the feature extraction algorithms used to process the data. Often the data produced is used without any quality control (QC) of such flaws by the user or the software.
Users may visually check an array to see if there are obvious flaws (e.g. streaks due to hyb/wash problems; incorrect feature positioning by the feature extraction software; etc). However, this is a very time-consuming and subjective process, not lending itself to production of metrics that can be tracked over time.
Some currently available software may report QC metrics such as overall signal level or average signal and standard deviation of signal of specific probes. However, these metrics may not cover the entire range of problems that may occur and make trouble-shooting difficult as to which upstream process may be flawed. Currently available QC software may not account for internal details of the processes to which arrays are subjected, e.g., such as array design, probe synthesis, target prep/labeling, array hyb/wash/scan and/or feature extraction. Different error modes may occur depending upon the type of processes used upstream of the data analysis step(s).
Users may have preferences to see certain metrics and not others, depending upon their experiments. Metrics may be reported without threshold warnings. Users often desire performance metrics such as “sensitivity”, “dynamic range”, “linearity” etc. A problem with these terms is that they can be can be defined in many different manners causing a lack of standardization across platforms and/or experiments. Additionally, these definitions may not be appropriate for all array experimental conditions.
Users may have difficulties in interpreting array data due to incorrect algorithms being used (e.g. background-subtraction, dye-normalization algorithms and the like) and not have metrics that readily aid in this type of evaluation.
There remains a need for quality control solutions for objectively determining the quality of chemical arrays covering a variety of different experiments and different experimental conditions employed that may require a variety of different metrics to be employed to identify errors, or lack thereof, that may occur when using any of these variety of conditions and experiments. A variety of metrics are needed to capture a wide range of potential upstream process problems that can affect the quality of a chemical array produced by such processes.